def delete_model(self, model):
"""Deletes a model.
"""
check_resource_type(model, MODEL_PATH, message="A model id is needed.")
model_id = get_model_id(model)
if model_id:
return self._delete("%s%s" % (self.url, model_id))
def create_prediction(self, model, input_data=None,
args=None, wait_time=3, retries=10, by_name=True):
"""Creates a new prediction.
The model parameter can be:
- a simple tree model
- a simple logistic regression model
- an ensemble
The by_name argument is now deprecated. It will be removed.
"""
logistic_regression_id = None
ensemble_id = None
model_id = None
resource_type = get_resource_type(model)
if resource_type == ENSEMBLE_PATH:
ensemble_id = get_ensemble_id(model)
if ensemble_id is not None:
check_resource(ensemble_id,
query_string=TINY_RESOURCE,
wait_time=wait_time, retries=retries,
raise_on_error=True, api=self)
elif resource_type == MODEL_PATH:
model_id = get_model_id(model)
check_resource(model_id,
query_string=TINY_RESOURCE,
wait_time=wait_time, retries=retries,
raise_on_error=True, api=self)
elif resource_type == LOGISTIC_REGRESSION_PATH:
logistic_regression_id = get_logistic_regression_id(model)
check_resource(logistic_regression_id,
query_string=TINY_RESOURCE,
wait_time=wait_time, retries=retries,
raise_on_error=True, api=self)
else:
raise Exception("A model or ensemble id is needed to create a"
" prediction. %s found." % resource_type)
if input_data is None:
input_data = {}
create_args = {}
if args is not None:
create_args.update(args)
create_args.update({
"input_data": input_data})
if model_id is not None:
create_args.update({
"model": model_id})
elif ensemble_id is not None:
create_args.update({
"ensemble": ensemble_id})
elif logistic_regression_id is not None:
create_args.update({
"logisticregression": logistic_regression_id})
body = json.dumps(create_args)
return self._create(self.prediction_url, body,
verify=self.verify_prediction)
def create_prediction(self, model, input_data=None,
args=None, wait_time=3, retries=10, by_name=True):
"""Creates a new prediction.
The model parameter can be:
- a simple tree model
- a simple logistic regression model
- an ensemble
The by_name argument is now deprecated. It will be removed.
"""
logistic_regression_id = None
ensemble_id = None
model_id = None
resource_type = get_resource_type(model)
if resource_type == ENSEMBLE_PATH:
ensemble_id = get_ensemble_id(model)
if ensemble_id is not None:
check_resource(ensemble_id,
query_string=TINY_RESOURCE,
wait_time=wait_time, retries=retries,
raise_on_error=True, api=self)
elif resource_type == MODEL_PATH:
model_id = get_model_id(model)
check_resource(model_id,
query_string=TINY_RESOURCE,
wait_time=wait_time, retries=retries,
raise_on_error=True, api=self)
elif resource_type == LOGISTIC_REGRESSION_PATH:
logistic_regression_id = get_logistic_regression_id(model)
check_resource(logistic_regression_id,
query_string=TINY_RESOURCE,
wait_time=wait_time, retries=retries,
raise_on_error=True, api=self)
else:
raise Exception("A model or ensemble id is needed to create a"
" prediction. %s found." % resource_type)
if input_data is None:
input_data = {}
create_args = {}
if args is not None:
create_args.update(args)
create_args.update({
"input_data": input_data})
if model_id is not None:
create_args.update({
"model": model_id})
elif ensemble_id is not None:
create_args.update({
"ensemble": ensemble_id})
elif logistic_regression_id is not None:
create_args.update({
"logisticregression": logistic_regression_id})
body = json.dumps(create_args)
return self._create(self.prediction_url, body,
verify=self.verify_prediction)
def delete_model(self, model):
"""Deletes a model.
"""
check_resource_type(model, MODEL_PATH,
message="A model id is needed.")
model_id = get_model_id(model)
if model_id:
return self._delete("%s%s" % (self.url, model_id))
def update_model(self, model, changes):
"""Updates a model.
"""
check_resource_type(model, MODEL_PATH, message="A model id is needed.")
model_id = get_model_id(model)
if model_id:
body = json.dumps(changes)
return self._update("%s%s" % (self.url, model_id), body)
def update_model(self, model, changes):
"""Updates a model.
"""
check_resource_type(model, MODEL_PATH,
message="A model id is needed.")
model_id = get_model_id(model)
if model_id:
body = json.dumps(changes)
return self._update("%s%s" % (self.url, model_id), body)
def get_model(self, model, query_string='',
shared_username=None, shared_api_key=None):
"""Retrieves a model.
The model parameter should be a string containing the
model id or the dict returned by create_model.
As model is an evolving object that is processed
until it reaches the FINISHED or FAULTY state, the function will
return a dict that encloses the model values and state info
available at the time it is called.
If this is a shared model, the username and sharing api key must
also be provided.
"""
check_resource_type(model, MODEL_PATH,
message="A model id is needed.")
model_id = get_model_id(model)
if model_id:
return self._get("%s%s" % (self.url, model_id),
query_string=query_string,
shared_username=shared_username,
shared_api_key=shared_api_key)
def get_model(self, model, query_string='',
shared_username=None, shared_api_key=None):
"""Retrieves a model.
The model parameter should be a string containing the
model id or the dict returned by create_model.
As model is an evolving object that is processed
until it reaches the FINISHED or FAULTY state, the function will
return a dict that encloses the model values and state info
available at the time it is called.
If this is a shared model, the username and sharing api key must
also be provided.
"""
check_resource_type(model, MODEL_PATH,
message="A model id is needed.")
model_id = get_model_id(model)
if model_id:
return self._get("%s%s" % (self.url, model_id),
query_string=query_string,
shared_username=shared_username,
shared_api_key=shared_api_key)
